Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields

	Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields
	Yu, Haoyang 1; Gao, Lianru 1; Li, Jun 1; Li, Shan Shan 1; Zhang, Bing 1; Benediktsson, Jón Atli 1
刊名	Remote Sensing
	2016
卷号	8 期号:4
关键词	EAST CHINA SEA ABSORPTION-COEFFICIENTS OPTICAL CLASSIFICATION ATMOSPHERIC CORRECTION WATERS PHYTOPLANKTON REFLECTANCE VARIABILITY ALGORITHMS PRODUCTS
通讯作者	Gao, Lianru (gaolr@radi.ac.cn)
英文摘要	This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial information of the image pixels is modeled using an adaptive Markov random field (MRF) method. Finally, the maximum posterior probability classification is computed via the simulated annealing (SA) optimization algorithm. The combination of subspace-based SVMs and adaptive MRFs is the main contribution of this paper. The resulting methods, called SVMsub-eMRF and SVMsub-aMRF, were experimentally validated using two typical real hyperspectral data sets. The obtained results indicate that the proposed methods demonstrate superior performance compared with other classical hyperspectral image classification methods. © 2016 by the authors.
学科主题	Remote Sensing
类目[WOS]	Remote Sensing
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20162302478807
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39241]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100094, China 3. The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 4.100049, China 5. School of Geography and Planning, Sun Yat-sen University, Guangzhou 6.510275, China 7. Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 8.107, Iceland
推荐引用方式 GB/T 7714	Yu, Haoyang,Gao, Lianru,Li, Jun,et al. Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields[J]. Remote Sensing,2016,8(4).
APA	Yu, Haoyang,Gao, Lianru,Li, Jun,Li, Shan Shan,Zhang, Bing,&Benediktsson, Jón Atli.(2016).Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields.Remote Sensing,8(4).
MLA	Yu, Haoyang,et al."Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields".Remote Sensing 8.4(2016).